So far, the focus of this study has been on designing the acoustic model. However, performance of the acoustic model can further improve by deploying more robust input features other than MFCC. In the final section, we evaluate the proposed method trained on noise-invariant Wav2Vec features [1]}. Wav2Vec representation has been trained on large amounts of unlabeled audio data in an unsupervised manner. Fig. REF  (left) shows the WER of the baseline ResNet18 trained on MFCC and Wav2Vec features, which manifests the effectiveness of Wav2Vec in reducing the WER across all test sets in the absence of speaker auxiliary information. The highest improvement is achieved for overlap speech with SIR 15dB, which is \(+11\%\)  absolute improvement in WER. Fig.REF  (right) depicts the WER of the proposed speaker conditioned ResNet18 trained on MFCC and Wav2Vec. Similar to the baseline, the speaker conditioned acoustic model benefits from the Wav2Vec features by achieving \(+6\%\)  absolute improvement in WER for SIR 15dB. However, due to the availability of speaker information, the acoustic model is less sensitive to the robustness of the input acoustic features, and thus, the amount of improvement from Wav2Vec is less in the proposed speaker conditioned ResNet18 compared to the baseline. In conclusion, the WER across all test sets is improved by using the proposed speaker conditioned acoustic model trained on wav2Vec. For example, on the overlap speech test set with SIR 15dB, the proposed ResNet18 with Affine Transformation trained on Wav2Vec gains +33% relative (+11% absolute) improvement in WER compared to the original ResNet trained on MFCC.
